It turns out that when you submit a paper to a journal like Nature Geoscience “just in case, we have nothing to lose, they’ll probably reject it straight away”…sometimes you are unexpectedly successful.

You may have already heard that carbon dioxide concentrations have surpassed 400 ppm. The most famous monitoring station, Mauna Loa Observatory in Hawaii, reached this value on May 9th. Due to the seasonal cycle, CO2 levels began to decline almost immediately thereafter, but next year they will easily blow past 400 ppm.

Of course, this milestone is largely arbitrary. There’s nothing inherently special about 400 ppm. But it’s a good reminder that while we were arguing about taxation, CO2 levels continued to quietly tick up and up.

In happier news, John Cook and others have just published the most exhaustive survey of the peer-reviewed climate literature to date. Read the paper here (open access), and a detailed but accessible summary here. Unsurprisingly, they found the same 97% consensus that has come up over and over again.

Cook et al read the abstracts of nearly 12 000 papers published between 1991 and 2011 – every single hit from the ISI Web of Science with the keywords “global climate change” or “global warming”. Several different people categorized each abstract, and the authors were contacted whenever possible to categorize their own papers. Using several different methods like this makes the results more reliable.

Around two-thirds of the studies, particularly the more recent ones, didn’t mention the cause of climate change. This is unsurprising, since human-caused warming has been common knowledge in the field for years. Similarly, seismology papers don’t usually mention that plate tectonics cause earthquakes, particularly in the abstracts where space is limited.

Among the papers which did express a position, 97.1% said climate change was human-caused. Again, unsurprising to anyone working in the field, but it’s news to many members of the public. The study has been widely covered in the mainstream media – everywhere from The Guardian to The Australian – and even President Obama’s Twitter feed.

Congratulations are also due to Andrew Weaver, my supervisor from last summer, who has just been elected to the British Columbia provincial legislature. He is not only the first-ever Green Party MLA in BC’s history, but also (as far as I know) the first-ever climate scientist to hold public office.

Governments the world over are sorely in need of officials who actually understand the problem of climate change. Nobody fits this description better than Andrew, and I think he is going to be great. The large margin by which he won also indicates that public support for climate action is perhaps higher than we thought.

Finally, my second publication came out this week in Climate of the Past. It describes an EMIC intercomparison project the UVic lab conducted for the next IPCC report, which I helped out with while I was there. The project was so large that we split the results into two papers (the second of which is in press in Journal of Climate). This paper covers the historical experiments – comparing model results from 850-2005 to observations and proxy reconstructions – as well as some idealized experiments designed to measure metrics such as climate sensitivity, transient climate response, and carbon cycle feedbacks.

It seems that every post I write begins with an apology for not writing more. I’ve spent the past few months writing another set of exams (only one more year to go), building and documenting two simple climate models for term projects (much more on that later), and moving to Australia!

This (Northern Hemisphere) summer I have a job at the Climate Change Research Centre at the University of New South Wales in Sydney, which has a close partnership with the UVic Climate Lab (where I worked last summer). I am working with Dr. Katrin Meissner, who primarily studies ocean, carbon cycle, and paleoclimate modelling. We have lots of plans for exciting projects to work on over the next four months.

Australia is an interesting place. Given that it’s nearly 20 hours away by plane, it has a remarkably similar culture to Canada. The weather is much warmer, though (yesterday it dropped down to 15 C and everyone was complaining about the cold) and the food is fantastic. The birds are more colourful (Rainbow Lorikeets are so common that some consider them pests) and the bats are as big as ravens. Best of all, there is an ocean. I think I am going to like it here.

Today my very first scientific publication is appearing in Geophysical Research Letters. During my summer at UVic, I helped out with a model intercomparison project regarding the effect of climate change on Atlantic circulation, and was listed as a coauthor on the resulting paper. I suppose I am a proper scientist now, rather than just a scientist larva.

The Atlantic meridional overturning circulation (AMOC for short) is an integral part of the global ocean conveyor belt. In the North Atlantic, a massive amount of water near the surface, cooling down on its way to the poles, becomes dense enough to sink. From there it goes on a thousand-year journey around the world – inching its way along the bottom of the ocean, looping around Antarctica – before finally warming up enough to rise back to the surface. A whole multitude of currents depend on the AMOC, most famously the Gulf Stream, which keeps Europe pleasantly warm.

Some have hypothesized that climate change might shut down the AMOC: the extra heat and freshwater (from melting ice) coming into the North Atlantic could conceivably lower the density of surface water enough to stop it sinking. This happened as the world was coming out of the last ice age, in an event known as the Younger Dryas: a huge ice sheet over North America suddenly gave way, drained into the North Atlantic, and shut down the AMOC. Europe, cut off from the Gulf Stream and at the mercy of the ice-albedo feedback, experienced another thousand years of glacial conditions.

A shutdown today would not lead to another ice age, but it could cause some serious regional cooling over Europe, among other impacts that we don’t fully understand. Today, though, there’s a lot less ice to start with. Could the AMOC still shut down? If not, how much will it weaken due to climate change? So far, scientists have answered these two questions with “probably not” and “something like 25%” respectively. In this study, we analysed 30 climate models (25 complex CMIP5 models, and 5 smaller, less complex EMICs) and came up with basically the same answer. It’s important to note that none of the models include dynamic ice sheets (computational glacial dynamics is a headache and a half), which might affect our results.

Models ran the four standard RCP experiments from 2006-2100. Not every model completed every RCP, and some extended their simulations to 2300 or 3000. In total, there were over 30 000 model years of data. We measured the “strength” of the AMOC using the standard unit Sv (Sverdrups), where each Sv is 1 million cubic metres of water per second.

Only two models simulated an AMOC collapse, and only at the tail end of the most extreme scenario (RCP8.5, which quite frankly gives me a stomachache). Bern3D, an EMIC from Switzerland, showed a MOC strength of essentially zero by the year 3000; CNRM-CM5, a GCM from France, stabilized near zero by 2300. In general, the models showed only a moderate weakening of the AMOC by 2100, with best estimates ranging from a 22% drop for RCP2.6 to a 40% drop for RCP8.5 (with respect to preindustrial conditions).

Are these somewhat-reassuring results trustworthy? Or is the Atlantic circulation in today’s climate models intrinsically too stable? Our model intercomparison also addressed that question, using a neat little scalar metric known as Fov: the net amount of freshwater travelling from the AMOC to the South Atlantic.

The current thinking in physical oceanography is that the AMOC is more or less binary – it’s either “on” or “off”. When AMOC strength is below a certain level (let’s call it A), its only stable state is “off”, and the strength will converge to zero as the currents shut down. When AMOC strength is above some other level (let’s call it B), its only stable state is “on”, and if you were to artificially shut it off, it would bounce right back up to its original level. However, when AMOC strength is between A and B, both conditions can be stable, so whether it’s on or off depends on where it started. This phenomenon is known as hysteresis, and is found in many systems in nature.

This figure was not part of the paper. I made it just now in MS Paint.

Here’s the key part: when AMOC strength is less than A or greater than B, Fov is positive and the system is monostable. When AMOC strength is between A and B, Fov is negative and the system is bistable. The physical justification for Fov is its association with the salt advection feedback, the sign of which is opposite Fov: positive Fov means the salt advection feedback is negative (i.e. stabilizing the current state, so monostable); a negative Fov means the salt advection feedback is positive (i.e. reinforcing changes in either direction, so bistable).

Most observational estimates (largely ocean reanalyses) have Fov as slightly negative. If models’ AMOCs really were too stable, their Fov‘s should be positive. In our intercomparison, we found both positives and negatives – the models were kind of all over the place with respect to Fov. So maybe some models are overly stable, but certainly not all of them, or even the majority.

As part of this project, I got to write a new section of code for the UVic model, which calculated Fov each timestep and included the annual mean in the model output. Software development on a large, established project with many contributors can be tricky, and the process involved a great deal of head-scratching, but it was a lot of fun. Programming is so satisfying.

Beyond that, my main contribution to the project was creating the figures and calculating the multi-model statistics, which got a bit unwieldy as the model count approached 30, but we made it work. I am now extremely well-versed in IDL graphics keywords, which I’m sure will come in handy again. Unfortunately I don’t think I can reproduce any figures here, as the paper’s not open-access.

I was pretty paranoid while coding and doing calculations, though – I kept worrying that I would make a mistake, never catch it, and have it dredged out by contrarians a decade later (“Kate-gate”, they would call it). As a climate scientist, I suppose that comes with the job these days. But I can live with it, because this stuff is just so darned interesting.

During my summer at UVic, two PhD students at the lab (Andrew MacDougall and Chris Avis) as well as my supervisor (Andrew Weaver) wrote a paper modelling the permafrost carbon feedback, which was recently published in Nature Geoscience. I read a draft version of this paper several months ago, and am very excited to finally share it here.

Studying the permafrost carbon feedback is at once exciting (because it has been left out of climate models for so long) and terrifying (because it has the potential to be a real game-changer). There is about twice as much carbon frozen into permafrost than there is floating around in the entire atmosphere. As high CO2 levels cause the world to warm, some of the permafrost will thaw and release this carbon as more CO2 – causing more warming, and so on. Previous climate model simulations involving permafrost have measured the CO2 released during thaw, but haven’t actually applied it to the atmosphere and allowed it to change the climate. This UVic study is the first to close that feedback loop (in climate model speak we call this “fully coupled”).

The permafrost part of the land component was already in place – it was developed for Chris’s PhD thesis, and implemented in a previous paper. It involves converting the existing single-layer soil model to a multi-layer model where some layers can be frozen year-round. Also, instead of the four RCP scenarios, the authors used DEPs (Diagnosed Emission Pathways): exactly the same as RCPs, except that CO2emissions, rather than concentrations, are given to the model as input. This was necessary so that extra emissions from permafrost thaw would be taken into account by concentration values calculated at the time.

As a result, permafrost added an extra 44, 104, 185, and 279 ppm of CO2 to the atmosphere for DEP 2.6, 4.5, 6.0, and 8.5 respectively. However, the extra warming by 2100 was about the same for each DEP, with central estimates around 0.25 °C. Interestingly, the logarithmic effect of CO2 on climate (adding 10 ppm to the atmosphere causes more warming when the background concentration is 300 ppm than when it is 400 ppm) managed to cancel out the increasing amounts of permafrost thaw. By 2300, the central estimates of extra warming were more variable, and ranged from 0.13 to 1.69 °C when full uncertainty ranges were taken into account. Altering climate sensitivity (by means of an artificial feedback), in particular, had a large effect.

As a result of the thawing permafrost, the land switched from a carbon sink (net CO2 absorber) to a carbon source (net CO2 emitter) decades earlier than it would have otherwise – before 2100 for every DEP. The ocean kept absorbing carbon, but in some scenarios the carbon source of the land outweighed the carbon sink of the ocean. That is, even without human emissions, the land was emitting more CO2 than the ocean could soak up. Concentrations kept climbing indefinitely, even if human emissions suddenly dropped to zero. This is the part of the paper that made me want to hide under my desk.

This scenario wasn’t too hard to reach, either – if climate sensitivity was greater than 3°C warming per doubling of CO2 (about a 50% chance, as 3°C is the median estimate by scientists today), and people followed DEP 8.5 to at least 2013 before stopping all emissions (a very intense scenario, but I wouldn’t underestimate our ability to dig up fossil fuels and burn them really fast), permafrost thaw ensured that CO2 concentrations kept rising on their own in a self-sustaining loop. The scenarios didn’t run past 2300, but I’m sure that if you left it long enough the ocean would eventually win and CO2 would start to fall. The ocean always wins in the end, but things can be pretty nasty until then.

As if that weren’t enough, the paper goes on to list a whole bunch of reasons why their values are likely underestimates. For example, they assumed that all emissions from permafrost were CO2, rather than the much stronger CH4 which is easily produced in oxygen-depleted soil; the UVic model is also known to underestimate Arctic amplification of climate change (how much faster the Arctic warms than the rest of the planet). Most of the uncertainties – and there are many – are in the direction we don’t want, suggesting that the problem will be worse than what we see in the model.

This paper went in my mental “oh shit” folder, because it made me realize that we are starting to lose control over the climate system. No matter what path we follow – even if we manage slightly negative emissions, i.e. artificially removing CO2 from the atmosphere – this model suggests we’ve got an extra 0.25°C in the pipeline due to permafrost. It doesn’t sound like much, but add that to the 0.8°C we’ve already seen, and take technological inertia into account (it’s simply not feasible to stop all emissions overnight), and we’re coming perilously close to the big nonlinearity (i.e. tipping point) that many argue is between 1.5 and 2°C. Take political inertia into account (most governments are nowhere near even creating a plan to reduce emissions), and we’ve long passed it.

Just because we’re probably going to miss the the first tipping point, though, doesn’t mean we should throw up our hands and give up. 2°C is bad, but 5°C is awful, and 10°C is unthinkable. The situation can always get worse if we let it, and how irresponsible would it be if we did?

Scientists do not blindly trust their own models of global warming. In fact, nobody is more aware of a model’s specific weaknesses than the developers themselves. Most of our time is spent comparing model output to observations, searching for discrepancies, and hunting down bugs.

If 1.5 C global warming above preindustrial temperatures really does represent the threshold for “dangerous climate change” (rather than 2 C, as some have argued), then we’re in trouble. Stabilizing global temperatures at this level isn’t just climatically difficult, it’s also mathematically difficult. Given current global temperatures, and their current rate of change, it’s nearly impossible to smoothly extend the curve to stabilize at 1.5 C without overshooting.

Sometimes computers do weird things. Some bugs appear for the most illogical reasons (last week, the act of declaring a variable altered every single metric of the model output). Other bugs show up once, then disappear before you can track down the source, and you’re never able to reproduce them. It’s not uncommon to fix a problem without ever understanding why the problem occurred in the first place.

For anyone working with climate model output, one of the best tools to have in your arsenal is the combination of IDL and NetCDF. Hardly an hour of work goes by when I don’t use one or both of these programming tools in some way.

Developing model code for the first time is a lot like moving to a new city. At first you wander around aimlessly, clutching your map and hesitantly asking for directions. Then you begin to recognize street names and orient yourself around landmarks. Eventually you’re considered a resident of the city, as your little house is there on the map with your name on it. You feel inordinately proud of the fact that you managed to build that house without burning the entire city down in the process.

The RCP 8.5 scenario is really, really scary. Looking at the output from that experiment is enough to give me a stomachache. Let’s just not let that scenario happen, okay?

It’s entirely possible to get up in the morning and just decide to be enthusiastic about your work. You don’t have to pretend, or lie to yourself – all you do is consciously choose to revel in the interesting discoveries, and to view your setbacks as challenges rather than chores. It works really well, and everything is easier and more fun as a result.

Climate models are fabulous experimental subjects. If you run the UVic model twice with the same code, data, options, and initial state, you get exactly the same results. (I’m not sure if this holds for more complex GCMs which include elements of random weather variation.) For this reason, if you change one factor, you can be sure that the model is reacting only to that factor. Control runs are completely free of external influences, and deconstructing confounding variables is only a matter of CPU time. Most experimental scientists don’t have this element of perfection in their subjects – it makes me feel very lucky.

The permafrost is in big trouble, and scientists are remarkably calm about it.

Tasks that seem impossible at first glance are often second nature by the end of the day. No bug lasts forever, and no problem goes unsolved if you exert enough effort.

About

Kaitlin Naughten is a PhD student in climate science at the University of New South Wales in Sydney, Australia. She became interested in climate science as a teenager on the Canadian Prairies, and increasingly began to notice the discrepancies between scientific and public knowledge on climate change. She started writing this blog at age sixteen to help address this gap in public understanding, and it slowly evolved into a record of her research as a young climate scientist. Read more

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